Project Summary/Abstract My goal is to become an independent investigator in human genetics focusing on understanding the molecular basis of cardiovascular (CV) diseases such as congenital heart disease (CHD). CHD affects ~1% of live births, and there are now more adults with CHD than children. Although CHD has a strong genetic component, the causative mechanisms remain poorly understood. As part of the Pediatric Cardiac Genomics Consortium (PCGC), we have performed whole exome sequencing (WES) on 3,443 case trios from > 13,000 patients recruited into the study. We found that de novo mutations (DNMs) underlie 10% of cases and rare inherited mutations contribute to ~1.8% of cases. Together with environmental risk factors, copy number variation, and aneuploidy, these findings only explain ~45% of CHD. My central hypothesis is that a subset of CHD cases result from the epistatic interaction of rare and common variants in the same biological pathway and that polygenic inheritance can account for some proportion of unexplained CHD cases. Moreover, I hypothesize that a combined analysis of de novo and transmitted variations has enhanced power to identify additional CHD risk genes. I propose three aims that will utilize my background in statistical genetics to CHD genetics. In Aim 1, I will identify genetic modifiers of FLT4, a gene we have shown that loss of function mutations cause 2.3% of Tetralogy of Fallot, albeit with striking incomplete penetrance. I will apply a hypothesis-based candidate gene approach to study how common variants in modifier genes modulate the expressivity of driver mutations in FLT4 by jointly analyzing WES and SNP array data from ~2,500 European CHD trios. I will then analyze WES data from 3,443 CHD trios to determine if there is significant transmission disequilibrium for FLT4 missense mutations. In Aim 2, I will perform an integrated analysis of DNMs, rare inherited variants, and de novo CNVs to identify additional CHD genes that could not be identified when modeling different types of genetic variants separately. In Aim 3, I will analyze SNP array and WES data in ~2,500 European CHD trios to investigate the combined effects of common polygenic variants and DNMs. Further, I will use a genome-wide polygenic risk score (PRS) method to identify patients with a high PRS equivalent to the risk introduced by a monogenic pathogenic mutation. In the K99 phase, I will receive training in both genome & structural variation analyses and cardiac genetics & physiology. Following my K99 training, I will use these techniques to develop bioinformatics pipelines for the integrated analysis of common polygenic and rare variants in CV diseases as I transition to independence. This proposal will identify the genetic underpinnings of some proportion of unexplained cases, allowing new insight into mechanisms governing disease development, and the opportunity to mitigate these risks. I will distinguish my research from my mentors? by developing statistical methods for the integration of multi-omic data and complex genetic models in CV disease and extend the understanding of CV disease genetics from rare variants with a large effect to the contribution of complex genetics.